Enterprise metadata is essential for data discovery, governance, integration, and analytics. Traditional metadata engineering relies on manual, rule-based approaches that struggle with dynamic, heterogeneous enterprise data across cloud, IoT, ERP, CRM, and data lake environments. This study proposes a Large Language Model-Assisted Metadata Engineering Framework (LLM-MEF) that automates metadata extraction, semantic enrichment, schema recommendation, lineage discovery, and governance validation using transformer-based LLMs, Retrieval-Augmented Generation (RAG), vector databases, and knowledge graphs. The framework improves metadata quality, semantic consistency, discoverability, governance compliance, and operational efficiency while reducing manual effort. Explainable AI and continuous feedback learning further enhance transparency and adaptive improvement. The proposed LLM-MEF provides a scalable and intelligent solution for enterprise metadata management, supporting modern data governance, AI, business intelligence, regulatory compliance, and digital transformation.
David Wheeler, Michael Gordon· International Journal of Dat...· 0 citations
Predictive Decision Support Systems (PDSS) increasingly rely on machine learning, but conventional models often provide deterministic predictions without measuring uncertainty, limiting their reliability in high-stakes applications. This paper proposes an uncertainty-aware machine learning framework that integrates uncertainty quantification, probabilistic modeling, explainable AI, and adaptive learning to improve prediction reliability, transparency, and decision confidence. The framework models both aleatoric and epistemic uncertainties, producing confidence scores, prediction intervals, and interpretable decision reports instead of single-point predictions. By combining intelligent data preprocessing, uncertainty-aware learning, trustworthy decision intelligence, and continuous model adaptation, the proposed approach enhances robustness, calibration, explainability, and trustworthiness, providing a scalable foundation for reliable predictive decision support in dynamic real-world environments.
David Wheeler, Michael Gordon· International Journal of Mac...· 0 citations
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